Optimal Portfolio Rule: When There is Uncertainty in The Parameter Estimates
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Date
2012-04-11T15:33:33Z
Authors
Jin, Hyunjong
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
The classical mean-variance model, proposed by Harry Markowitz in 1952, has been one
of the most powerful tools in the field of portfolio optimization. In this model, parameters are estimated by their sample counterparts. However, this leads to estimation risk, which the model completely ignores. In addition, the mean-variance model fails to incorporate behavioral aspects of investment decisions. To remedy the problem, the notion of ambiguity
aversion has been addressed by several papers where investors acknowledge uncertainty in the estimation of mean returns. We extend the idea to the variances and correlation coefficient of the portfolio, and study their impact. The performance of the portfolio is measured in terms of its Sharpe ratio. We consider different cases where one parameter is assumed to be perfectly estimated by the sample counterpart whereas the other parameters introduce ambiguity, and vice versa, and investigate which parameter has what impact on the performance of the portfolio.
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Keywords
Uncertainty, Ambiguity, Portfolio Optimization